RNA-RNA interactions (RRIs) can dictate RNA molecules to form intricate higher-order structures and bind their RNA substrates in diverse biological processes. To elucidate the function, binding specificity, and regulatory mechanisms of various RNA molecules, especially the vast repertoire of non-coding RNAs, advanced technologies and methods that globally map RRIs are extremely valuable. In the past decades, many state-of-the-art technologies have been developed for this purpose. This review focuses on those high-throughput technologies for the global mapping of RRIs. We summarize the key concepts and the pros and cons of different technologies. In addition, we highlight the novel biological insights uncovered by these RRI mapping methods and discuss the future challenges for appreciating the crucial roles of RRIs in gene regulation across bacteria, viruses, archaea, and mammals.

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA\'s features for effective RRI predictions.

Granulosa cells (GCs) are essential for follicular development, and long non-coding RNAs (LncRNAs) are known to support the maintenance of this process and hormone synthesis in mammals. Nevertheless, the regulatory roles of these lncRNAs within sheep follicular GCs remain largely unexplored. This study delved into the influence of a Loc105611671, on the proliferation and steroid hormone synthesis of sheep ovarian GCs and the associated target genes in vitro. Cell Counting Kit-8 (CCK-8) gain-of-function experiments indicated that overexpression of Loc105611671 significantly boosted GCs proliferation, along with estrogen (E2) and progesterone (P4) levels. Further mechanistic scrutiny revealed that Loc105611671 is primarily localized within the cytoplasm of ovarian granulosa cells and engages in molecular interplay with CDC42. This interaction results in the upregulation of CDC42 protein expression. Moreover, it was discerned that increased CDC42 levels contribute to augmented proliferation of follicular granulosa cells and the secretion of E2 and P4. Experiments involving co-transfection elucidated that the concurrent overexpression of CDC42 and Loc105611671 acted synergistically to potentiate these effects. These findings provide insights into the molecular underpinnings of fecundity in ovine species and may inform future strategies for enhancing reproductive outcomes.

Multi-segmented viruses often multimerize their genomic segments to ensure efficient and stoichiometric packaging of the correct genetic cargo. In the bipartite Nodaviridae family, genome heterodimerization is also observed and conserved among different species. However, the nucleotide composition and biological function for this heterodimer remain unclear. Using Flock House virus as a model system, we developed a next-generation sequencing approach (\"XL-ClickSeq\") to probe heterodimer site sequences. We identified an intermolecular base-pairing site which contributed to heterodimerization in both wild-type and defective virus particles. Mutagenic disruption of this heterodimer site exhibited significant deficiencies in genome packaging and encapsidation specificity to viral genomic RNAs. Furthermore, the disruption of this intermolecular interaction directly impacts the thermostability of the mature virions. These results demonstrate that the intermolecular RNA-RNA interactions within the encapsidated genome of an RNA virus have an important role on virus particle integrity and thus may impact its transmission to a new host.IMPORTANCEFlock House virus is a member of Nodaviridae family of viruses, which provides a well-studied model virus for non-enveloped RNA virus assembly, cell entry, and replication. The Flock House virus genome consists of two separate RNA molecules, which can form a heterodimer upon heating of virus particles. Although similar RNA dimerization is utilized by other viruses (such as retroviruses) as a packaging mechanism and is conserved among Nodaviruses, the role of heterodimerization in the Nodavirus replication cycle is unclear. In this research, we identified the RNA sequences contributing to Flock House virus genome heterodimerization and discovered that such RNA-RNA interaction plays an essential role in virus packaging efficiency and particle integrity. This provides significant insight into how the interaction of packaged viral RNA may have a broader impact on the structural and functional properties of virus particles.

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial pathogen accounting for high mortality rates among infected patients. Transcriptomic regulation by small RNAs (sRNAs) has been shown to regulate networks promoting antibiotic resistance and virulence in S. aureus. Yet, the biological role of most sRNAs during MRSA host infection remains unknown. To fill this gap, in collaboration with the lab of Jai Tree, we performed comprehensive RNA-RNA interactome analyses in MRSA using CLASH under conditions that mimic the host environment. Here we present a detailed version of this optimized CLASH (cross-linking, ligation, and sequencing of hybrids) protocol we recently developed, which has been tailored to explore the RNA interactome in S. aureus as well as other Gram-positive bacteria. Alongside, we introduce a compilation of helpful Python functions for analyzing folding energies of putative RNA-RNA interactions and streamlining sRNA and mRNA seed discovery in CLASH data. In the accompanying computational demonstration, we aim to establish a standardized strategy to evaluate the likelihood that observed chimeras arise from true RNA-RNA interactions.

SARS-CoV-2 RNA interacts with host factors to suppress interferon responses and simultaneously induces cytokine release to drive the development of severe coronavirus disease 2019 (COVID-19). However, how SARS-CoV-2 hijacks host RNAs to elicit such imbalanced immune responses remains elusive. Here, we analyzed SARS-CoV-2 RNA in situ structures and interactions in infected cells and patient lung samples using RIC-seq. We discovered that SARS-CoV-2 RNA forms 2,095 potential duplexes with the 3\' UTRs of 205 host mRNAs to increase their stability by recruiting RNA-binding protein YBX3 in A549 cells. Disrupting the SARS-CoV-2-to-host RNA duplex or knocking down YBX3 decreased host mRNA stability and reduced viral replication. Among SARS-CoV-2-stabilized host targets, NFKBIZ was crucial for promoting cytokine production and reducing interferon responses, probably contributing to cytokine storm induction. Our study uncovers the crucial roles of RNA-RNA interactions in the immunopathogenesis of RNA viruses such as SARS-CoV-2 and provides valuable host targets for drug development.

The elaborate interplay of coding and noncoding factors governs muscle growth and development. Here, we reported a mutual activation between long noncoding RNA (lncRNA) H19 and MyoD (myogenic determination gene number 1) in the muscle process. We successfully cloned the two isoforms of goat H19, which were significantly enriched and positively correlated with MyoD transcripts in skeletal muscles or differentiating muscle satellite cells (MuSCs). To systematically screen genes altered by H19, we performed RNA-seq using cDNA libraries of differentiating H19-deficiency MuSCs and consequently anchored MyoD as the critical genes in mediating H19 function. Intriguingly, some transcripts of MyoD and H19 overlapped in the cytoplasm, which was dramatically damaged when the core complementary nucleotides were mutated. Meanwhile, MyoD RNA successfully pulled down H19 in MS2-RIP experiments. Furthermore, HuR could bind both H19 and MyoD transcripts, while H19 or its truncated mutants successfully stabilized MyoD mRNA, with or without HuR deficiency. In turn, novel functional MyoD protein-binding sites were identified in the promoter and exons of the H19 gene. Our results suggest that MyoD activates H19 transcriptionally, and RNA-RNA hybridization is critical for H19-promoted MyoD expression, which extends our knowledge of the hierarchy of regulatory networks in muscle growth.

In recent years, several genome-wide approaches based on RNA sequencing (RNA-seq) have been developed. These methods allow a comprehensive and dynamic view of the structure and function of the multi-layered RNA pathways and networks. Many of these approaches, including the promising one of single-cell transcriptome analysis, have been successfully applied to Pseudomonas aeruginosa. However, we are only at the beginning because only a few surrounding conditions have been considered. Here, we aim to illustrate the different types of approaches based on RNA-seq that will lead us in the future to a better understanding of the dynamics of RNA biology in P. aeruginosa.

Long non-coding RNAs have gained attention in recent years as they were shown to play crucial roles in the regulation of cellular processes, but the understanding of the exact mechanisms is still incomplete in most cases. This is also true for long non-coding RNA LINC00941, which was recently found to be highly upregulated in various types of cancer influencing cell proliferation and metastasis. Initial studies could not elucidate the mode of action to understand the role and real impact of LINC00941 in tissue homeostasis and cancer development. However, recent analyses have demonstrated multiple potential modes of action of LINC00941 influencing the functionality of various cancer cell types. Correspondingly, LINC00941 was proposed to be involved in regulation of mRNA transcription and modulation of protein stability, respectively. In addition, several experimental approaches suggest a function of LINC00941 as competitive endogenous RNA, thus acting in a post-transcriptional regulatory fashion. This review summarizes our recent knowledge about the mechanisms of action of LINC00941 elucidated so far and discusses its putative role in miRNA sequestering processes. In addition, the functional role of LINC00941 in regulating human keratinocytes is discussed to also highlight its role in normal tissue homeostasis tissue aside from its involvement in cancer.

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.